How AI Is Providing Digital Twins For Predictive Maintenance In Oil And Gas

The oil & gas industry still faces serious challenges, including the costs of maintaining its aging infrastructure. On average, 42% of offshore equipment is more than 15 years old and has 13% downtime – the vast majority of which is unplanned. It is estimated that refiners in the US alone lose around $6.6B a year due to unplanned downtimes.

For more than a decade, the rallying cry has been to turn to massive data collection and analytics. The numbers are substantial. Pipeline inspection generates 1.5TB for every 600km inspected, ultrasound around 1.2TB for every 8 hours of scanning, process data collected is around 6GB per plant per day and seismic surveys generate around 10TB each.

Unfortunately, this data has not yet turned out to be “the new oil” many predicted. 60% of operators still cite delivering outcomes from data as a major problem. The industry struggles to unlock end-to-end insights from the data it has been collecting. More than 95% is never used, and hand-made analytics are too slow and don’t scale well.

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The reliance on traditional analytics techniques, and a legacy CPU-compute infrastructure that lacks the needed processing power to analyze the volume and variety of data fast enough has also hampered progress. Oil and gas data needs algorithms and compute that can scale to distill these oceans of data, deliver insights and maintain efficiency.

The larger ecosystem of vendors has started to respond in the oil and gas sector. For example, companies such as MapD are using GPUs to provide in-memory database and real-time interactive visualization solutions benefitting from GPU-accelerated data aggregation to visualize millions of rows of data in real time, spanning drilling, production, and supply chains operations for better data utilization and timely decision support.

Deep learning, GPUs and the concept of “Digital Twins” offer enormous potential benefits for predictive maintenance in oil and gas. For example, early and accurate detection of faults, to predict remaining useful life of an asset given an operational context or to even prescribe guidance on work scope for the field service team with recommendations of parts and personnel skill desired to service them.

By definition, a “Digital Twin” is a continuously learning system of digital copies of assets, systems and processes that can be queried automatically, or even by voice, for specific outcomes. A digital twin can predict asset behavior and capacity to deliver on specific outcomes within given parameters and cost constraints. The machine doesn’t sleep and the digital twin informs what you need to do with the assets on the physical twin in order to achieve the targeted outcome.

The digital twin is a system of systems based on a virtual digital copy of all the infrastructure assets as represented by Deep Learning Neural Networks (DNNs). DNNs are a Machine Learning technique that brings us a new paradigm of “software that writes software” and acts as a compiler for your data to deliver the desired outcome.

Deep Learning, combined with domain specific physics-based modeling techniques, is finally making the concept of the digital twin a reality across many industrial sectors. NVIDIA and Baker Hughes GE are working on end-to-end platforms to build out full-stream AI-enabled services for major oil and gas operators across the globe and enable the vision of the digital twin.

Baker Hughes GE is seeing improved data processing abilities at 100 times higher resolution at the data source, data scientist productivity at 50 times faster model-building and training, and non-productive time availability (NPTA) with a timely and robust failure prediction. As Arun Subramaniyan, VP Data Science and Analytics at Baker Hughes, a GE Company, (BHGE) recently said: “Deep learning, enabled by GPU-accelerated compute, is instrumental in realizing the potential of industrial digital twins.” To realize the digital-twin vision for the oil & gas industry at scale, BHGE has embraced edge-to-cloud GPU-accelerated compute. At the 2018 Unify Conference in Houston, BHGE demonstrated predictive maintenance capabilities and multi-well field level optimization on a cluster of 500 wells in 90 seconds— one million times faster than what’s available on the market today. Such groundbreaking digital twin capabilities are offered by BHGE via their edge-to-cloud Applied AI platform powered by NVIDIA GPUs, as shown in the picture below, and is becoming a core element of their fit-for-purpose digital transformation solutions like IntelliStream™.

NVIDIA

The value proposition for this technology quickly becomes evident once businesses begin implementation. For example, high-producing electric submersible pumps (ESPs) can be monitored at less than 5% false alarm rate while detecting anomalies at 93% with up to 60 days of lead time.

This has the potential to deliver estimated NPT cost-avoidance benefits of $300K annually per well.

C-suite executives ask me how they can explore and take advantage of AI and deep learning to explore the potential of the digital twin for predictive maintenance. The answer is clear: Engage now. The technology is here, and it is time to adopt the AI-first culture. Begin a conversation on how best to build out your AI capability efficiently using the right tools and infrastructure.

This needs to be systematic and based on a single end-to-end AI and deep learning platform and architecture -- from edge to datacenter to cloud -- to support the constant calibration and continuous learning desired to model the evolving asset condition under varying environmental and operational parameters. Businesses also need to invest in building a deep learning infrastructure and skillsets by leveraging NVIDIA’s ecosystem of partners and its Deep Learning Institute, where companies can access both self-paced and on-site curriculums.

There is no doubt that smarter, faster, AI-powered applications and digital twins can have a fundamental effect on predictive maintenance in the oil & gas industry, but it will also pave the way for AI and GPU infrastructure enabled edge-to-cloud capabilities to enable full-stream outcomes for production optimization, automated drilling and even bridging the silos across all the operational streams from oil exploration and extraction to processing and distribution.

Piyush Modi is a Business Development/Strategist for Industrial Sector at NVIDIA. He is actively engaged with major industrial companies and related research labs to conceive and realize Industrial AI enabled solutions. He is interested in real time deep learning training/in...